文献类型：期刊文献

英文题名：Data-Driven Distributionally Robust Optimal Power Flow for Distribution Grids Under Wasserstein Ambiguity Sets

作者：Liu, Fangzhou[1];Huo, Jincheng[1];Liu, Fengfeng[2];Li, Dongliang[2];Xue, Dong[2]

机构：[1]Harbin Inst Technol, Natl Key Lab Modeling & Simulat Complex Syst, Harbin 150001, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：14

期号：4

外文期刊名：ELECTRONICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001431701600001)】；

基金：This research was funded in part by the National Natural Science Foundation of China under grant numbers 6237312 and 62173147.

语种：英文

外文关键词：chance-constrained programming; distributionally robust optimization; optimal power flow; Wasserstein metric

摘要：The increasing integration of distributed energy resources into distribution feeders introduces significant uncertainties, stemming from volatile renewable sources and other fluctuating electrical elements, which pose substantial challenges for optimal power flow (OPF) analysis. This paper introduces a data-driven distributionally robust chance-constrained (DRCC) approach to address the stochastic Alternating Current (AC) OPF problem in distribution grids, where the exact probability distributions of uncertainties are unknown. The proposed method utilizes the Wasserstein metric to construct an ambiguity set based on empirical distributions derived from historical data, eliminating the need for prior knowledge of the underlying probability distributions. Notably, the size of the Wasserstein ball within the ambiguity set is inversely related to the volume of available data, allowing for adaptive robustness. Moreover, a computationally efficient reformulation of the DRCC-OPF model is developed using the LinDistFlow AC power flow approximation. The effectiveness and precision of the developed method are validated through multiple IEEE distribution test cases, demonstrating higher reliability of the security constraints compared with other methods. As more data become available, this reliability is systematically and securely adjusted to achieve greater economic efficiency.